ObjectiveTo study the changing law of appearance color and physicochemical properties of Angelicae Sinensis Radix Carbonisata（ASRC） during the processing by color space method combined with statistical analysis， so as to provide reference for determining the processing endpoint and evaluating the quality of the decoction pieces. MethodsTaking processing time（4， 8， 12， 16 min） and temperature（180， 200， 220， 240 ℃） as factors， ASRC decoction pieces with different processing degrees were prepared in a completely randomized design. Then， the brightness value（L^*）， red-green value（a^*）， yellow-blue value（b^*）， and total chromaticity value （E^*ab） of the decoction pieces were determined by spectrophotometer， the color difference value（ΔE） was calculated， and the data of colorimetric values were analyzed by discriminant analysis. At the same time， the pH， charcoal adsorption， and contents of tannins， 5-hydroxymethylfurfural（5-HMF）， tryptophan， chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H and ligustilide of ASRC with different processing degrees were determined by pH meter， ultraviolet and visible spectrophotometry and ultra-high performance liquid chromatography（UPLC）. Principal component analysis（PCA） was used to analyze the data of physicochemical indexes， after determining the processing technology of ASRC， the canonical discriminant function was established to distinguish the decoction pieces with different processing degrees， and leave-one-out cross validation was conducted. Finally， Pearson correlation analysis was used to explore the correlation between various physicochemical indexes and chromaticity values. ResultsWith the prolongation of the processing time， L^*， a^*， b^* and E^*ab all showed a decreasing trend， and the established discriminant model based on color parameters was able to distinguish ASRC with different processing degrees. The pH showed an increasing trend with the prolongation of processing time， and the charcoal adsorption， and the contents of tannins， 5-HMF， and tryptophan all showed an increasing and then decreasing trend. Among them， the charcoal adsorption， contents of tannin and 5-HMF reached their maximum values successively after processing for 8-12 min. While the contents of chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H and ligustilide decreased with the increase of processing time， with a decrease of 60%-80% at 8 min of processing. Therefore， the optimal processing time should be determined to be 8-12 min. PCA could clearly distinguish ASRC with different processing degrees， while temperature had no significant effect on the processing degree. The 12 batches of process validation results（10 min， 180-240 ℃） showed that except for 3 batches identified as class Ⅱ light charcoal， all other batches were identified as class Ⅲ standard charcoal， and the chromaticity values of each batch of ASRC were within the reference range of class Ⅱ-Ⅲ sample chromaticity values. The correlation analysis showed that the chromaticity values were negatively correlated with pH and charcoal adsorption， and positively correlated with contents of tryptophan， chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H， and ligustilide. And both pH and charcoal adsorption were negatively correlated with the contents of the above components， but the charcoal adsorption was positively correlated with the content of 5-HMF. ConclusionThe chromaticity values and the contents of various physicochemical indicators of ASRC undergo significant changes with the prolongation of processing time， and there is a general correlation between chromaticity values and various physicochemical indicators. Based on the changes in color and physicochemical indicators， the optimal processing time for ASRC is determined to be 8-12 min. This study reveals the dynamic changes of the relevant indexes in the processing of ASRC， which can provide a reference for the discrimination of the processing degree and the quantitative study of the processing endpoint.

Objective To explore the role of neogambogic acid in the characteristics of colorectal cancer stem cells (CRC-CSCs) through the Wnt/β-catenin signaling pathway. Methods The colorectal cells SW480 and HCT166 were divided into control group and neogambogic acid groups (1.5, 3, 6, and 12 μmol/L). The viability of CRC-CSCs was determined by MTT method, and spheroid and clone formation assays were used to assess the capacity of spheroid formation and self-renewal ability of the cells. The effects of neogambogic acid on the apoptosis and cell cycle of CRC-CSCs were evaluated by flow cytometry assays. Real-time quantitative PCR was used to detect the mRNA expression levels of relative markers (CD133, CD44, ALDH1, Oct4, and Nanog) of CRC-CSCs, and the protein expression levels of the self-renewal marker (PCNA), apoptosis markers (cleaved caspase-3 and cleaved caspase-9), and Wnt/β-catenin signaling pathway markers (p-GSK3β, GSK3β, β-catenin, and Wnt) were analyzed using Western blot. Results Compared with the control group, after neogambogic acid treatment, the viability of SW480 and HCT116 cells decreased (P<0.05), the spheroid forming ability and the clone numbers of CRC-CSCs decreased (P<0.001, P<0.01) but the cell apoptosis rate increased (P<0.01), and cell cycle was arrested in G₀/G₁ phase. Moreover, neogambogic acid downregulated the mRNA and protein expression of relative markers of CRC-CSCs (CD133, CD44, ALDH1, Oct4, and Nanog), PCNA, p-GSK3β, β-catenin, and Wnt (P<0.05) and upregulated the expression of cleaved caspase-3, cleaved caspase-9, and GSK3β (P<0.01). Conclusion Neogambogic can inhibit the stem cell properties of colorectal cells via inhibition of Wnt/β-catenin signaling pathway. As a result, neogambogic acid may be an attractive agent against colorectal cancer.

Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

Objective:To investigate the effect of tripartite motif-containing protein 59 (TRIM59) on glucose metabolism in macrophages and its role in regulating hypoxia-inducible factor-1α (HIF-1α)/IL-10 axis in macrophages under inflammatory conditions.Methods:The differentially expressed genes between macrophages with high expression of TRIM59 and control cells transfected with empty TRIM59 plasmid were analyzed by GO and KEGG. The expression of HIF-1α by RAW264.7 macrophages with high expression of TRIM59 was detected at different time points after lipopolysaccharide (LPS) stimulation by RT-qPCR and Western blot. Bone marrow was isolated from TRIM59-cKO and TRIM59 flox/flox mice and induced to differentiate into bone marrow-derived macrophages (BMDMs). These BMDMs were stimulated with LPS and the supernatants of cell culture were collected at 3, 6, 12 and 24 h after stimulation to detect IL-10 level by ELISA. In addition, mouse models of cecal ligation and puncture (CLP) were established, and bronchoalveolar lavage fluid (BALF) samples were collected at the same time points to detect IL-10 level by ELISA. Histopathological changes in lung tissues were observed after HE staining. Results:There was a significant change in glucose metabolism-related genes in macrophages with high expression of TRIM59, and the content of lactic acid increased significantly. Compared with the control group, the expression of HIF-1α at mRNA level in BMDMs from TRIM59-cKO mice decreased after LPS stimulation ( P<0.05); the level of IL-10 increased at 3 h and 24 h in the TRIM59-cKO group, but there was no significant difference in IL-10 level at 6 h or 12 h between the two groups. In the TRIM59-cKO mouse model of CLP, the levels of IL-10 in the BALF samples increased with time, but decreased at 24 h. The level of IL-10 was higher in the TRIM59-cKO mouse model group than that in the control group at each time point ( P<0.05 or P<0.01). Conclusions:TRIM59 can inhibit inflammation and lung injury by decreasing HIF-1α-mediated lactate secretion and IL-10 expression in macrophages. This study provides a new idea for developing novel anti-sepsis drugs based on TRIM59.

Lymphatic metastasis is the main metastatic route for colorectal cancer, which increases the risk of cancer recurrence and distant metastasis. The properties of the lymph node metastatic colorectal cancer (LNM-CRC) cells are poorly understood, and effective therapies are still lacking. Here, we found that hypoxia-induced fibroblast activation protein alpha (FAPα) expression in LNM-CRC cells. Gain- or loss-function experiments demonstrated that FAPα enhanced tumor cell migration, invasion, epithelial-mesenchymal transition, stemness, and lymphangiogenesis via activation of the STAT3 pathway. In addition, FAPα in tumor cells induced extracellular matrix remodeling and established an immunosuppressive environment via recruiting regulatory T cells, to promote colorectal cancer lymph node metastasis (CRCLNM). Z-GP-DAVLBH, a FAPα-activated prodrug, inhibited CRCLNM by targeting FAPα-positive LNM-CRC cells. Our study highlights the role of FAPα in tumor cells in CRCLNM and provides a potential therapeutic target and promising strategy for CRCLNM.

Objective:To study clinical outcomes, genetic etiology, efficacy and safety of continuous renal replacement therapy (CRRT) for neonatal hyperammonemia.Methods:From September 2016 to June 2023, neonates with hyperammonemia receiving CRRT in NICU of our hospital were retrospectively analyzed. Their perinatal conditions, clinical manifestations, laboratory results, genetic tests, treatments and outcomes were collected. The patients were assigned into survival group and death group according to their conditions at discharge. SPSS 22.0 statistical software was used to analyze the differences between the two groups.Results:A total of 10 patients were enrolled, including 8 males and 2 females. The gestational age was 39.3(38.2,39.8)weeks and birth weight 3 300(3 050, 3 583) g. The age of onset was 2.0(2.0, 4.3) d. The main clinical manifestations included seizures, coma and high blood ammonia level (up to 586-1 250 μmol/L). The patients received CRRT at 3.0(2.0, 8.3) d of age and CRRT lasted for 20.5(16.5, 42.8) h. Before CRRT, average time of coma was 10.0(3.5, 12.8) h and the total duration of coma was 20.5(12.5, 29.0) h. After CRRT, blood ammonia decreased (52.6±22.2) μmol/L every hour for 6 h. The genetic tests showed ornithine transcarbamylase deficiency in 5 cases, methylmalonic acidemia in 2 cases, propionic acidemia in 1 case, carnitine acylcarnitine translocase deficiency in 1 case and transient hyperammonemia in 1 case. 6 patients survived. 4 patients died at discharge, including 2 withdrawal treatment. The duration of coma before CRRT and the total duration of coma in the death group were significantly longer than the survival group ( P<0.05). Conclusions:Inborn metabolic error are common causes of neonatal hyperammonemia. Timely CRRT can safely and effectively reduce blood ammonia levels and may improve clinical outcomes.

Objective To construct a nomogram prediction model for the risk of mild cognitive impair-ment (MCI) in elderly people aged ≥ 60-year-old.Methods A total of 502 elderly permanent residents in Guangxi were selected as the research subjects by the multi-stage stratified random sampling method,and the general situation questionnaire and the Beijing edition of MoCA-BJ scale were used to investigate the elderly people,and their anthropometric indicators were collected.The minimum absolute shrinkage rate and selection operator (LASSO) regression were used to screen the characteristic variables.The MCI risk nomogram pre-diction model was constructed.The receiver operating characteristic (ROC) curve and calibration curve were adopted to conduct the fitting effect test on the prediction model.Results Among the 502 elderly people,244 cases (46.04％) had the normal cognition and 258 cases (48.68％) had MCI.The logistic regression analysis showed that the age,education background,month income,children support,calf circumference,BMI and body fat index were the influencing factors of MCI in the elderly people,and the nomogram prediction model of the MCI risk in the elderly people was constructed by these seven variables.The area under the ROC curve (AUC) of the model was 0.790 (95％CI:0.750-0.829),the sensitivity was 0.64,the specificity was 0.62,the C-index index was 0.790,and the model fitting x2=8.111,P=0.454,the predictive value was basically consistent with the actual value.Conclusion The nomogram prediction model of MCI risk in the elderly peo-ple is successfully constructed with good predictive effect.

Objective To compare the efficiency of nomogram and different machine learning algo-rithms for constructing the dental caries predictive models for middle-aged and elderly people.Methods The multi-stage stratified random sampling method was used to select 510 middle-aged and elderly people from Nanning City,Guigang City and Chongzuo City as the research subjects for conducting the questionnaire sur-vey and oral cavity examination.The univariate analysis and Lasso regression were used to screen the related variables,and the multivariate logistic regression analysis was used to determine the final independent influen-cing factors.Based on the salient features,the nomogram predictive model was established,and the seven ma-chine learning algorithms,including linear discriminant analysis (LDA),partial least squares (PLS),range Doppler algorithm (RDA),generalized linear models (GLM),random forest (RF),support vector machine (SVM) kernel function (SVM-Radial),and SVM linear kernel function (SVM-Linear),were used to construct the seven kinds of dental caries risk predictive models.The area under the receiver operating characteristic (ROC) curve (AUC) was adopted to evaluate the predictive performance of various models and the predictive performance of models constructed using different variable screening methods.Results The detection rate of dental caries in middle-aged and elderly people was 71.18％.After feature screening,the five predictive factors were ultimately retained,which were the age (OR=0.945,95％CI:0.917-0.973),brushing frequency (OR=0.688,95％CI:0.475-0.997),whether having teeth cleaning in the past one year (OR=0.303,95％CI:0.103-0.890),number of remaining teeth (OR=1.062,95％CI:1.038-1.087) and oral health assess-ment tool (OHAT) score (OR=1.363,95％CI:1.234-1.505).The results of comparison of various models showed that the predictive model constructed by the RF algorithm performed the best,the median of AUC was 0.747,followed by the nomogram,and the median of AUC was 0.733.The median of AUCs in the predic-tion model constructed by single factor+Lasso+multivariate logistic (Lasso+logistic) screening independent variables were higher than those constructed by RF algorithm screening independent variables.ConclusionBased on Lasso+logistic screening variables,RF provide more reliable predictive efficiency in predicting dental caries in middle-aged and elderly people than nomogram and the other machine learning algorithms.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors, with rapid progression and a poor prognosis. More and more studies have shown that there are small open reading frames (sORFs) on the molecular sequences of a large number of non-coding RNAs (ncRNAs), which can encode conserved peptides that play an important role in controlling the occurrence and development of HCC. This article introduces the discovery, prediction, and validation methods of ncRNA-encoding polypeptides and reviews its research progress, with the aim of providing new targets and ideas for early-stage diagnosis, targeted therapy, and prognosis assessment of HCC.

Hepatocellular carcinoma (HCC) is the most common liver malignant tumor with complex pathogenesis and a poor prognosis. Metabolic reprogramming has been recognized as one of the important cancer markers, and the liver, as an important organ for lipid metabolism in the human body, plays an important role in the process of the occurrence and development of HCC. More and more evidence shows that long-chain non-coding RNA (lncRNA) can influence the lipid metabolism process by regulating key enzymes and transcription factors, as well as being involved in the occurrence and development of HCC. Therefore, explicating the mechanism of lncRNA in lipid metabolism reprogramming is conducive to providing new targets and strategies for the diagnosis and treatment and improving the prognosis of HCC patients. This article summarizes the latest research progress on the involvement of lncRNA in the reprogramming process of HCC lipid metabolism.